knowledge base enabled information filtering on social web -- emc

Knowledge-base Enabled Information Filtering on Social Web

Pavan Kapanipathi

Kno.e.sis Center, Wright State University

Advisor: Amit Sheth

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Kno.e.sis

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Social Web in 60 secs

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Social Web in 60 secs

500M users generate 500M tweets per day

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Disaster Management Organizations utilize Social Web

35% of 20M tweets during hurricane sandy shared information

and news about the disaster 5

Healthcare Issues

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Healthcare Issues

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Personalized Filtering on Social Web

Following Dynamically Evolving Topics as interests

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Personalization on Social Web

• Following Dynamically Evolving Topics • Indian Elections • US Elections • Heathcare Debate

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Personalization on Social Web

• Following Dynamically Evolving Topics • Indian Elections • US Elections • Heathcare Debate

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Dynamic Topics

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Dynamic Topics

Continuously Evolving on Twitter

Entity – Event relevance changes

Many entities are involved

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Dynamic Topics

Manually crawl using keywords

“indianelection” “jan25” “sandy”

“swineflu” “ebola”

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Dynamic Topics

Manually updating keywords to get topic relevant tweets is not

feasible

“indianelection” “modi” “bjp”

“congress”

“jan25” “egypt” “tunisia”

“arabspring”

“sandy” “newyork” “redcross” “fema”

“swineflu” “ebola”

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Problem

How can we automatically update the filters to track a dynamically

evolving topic on Twitter

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Hashtags as Filters

• Identify a topic on Twitter • Tweets with hashtags are

more informative • Users have a lot of freedom

to create them • Some get popular, most die

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Exploring Hashtags as Evolving Filters for Dynamic Topics

Colorado Shooting

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Colorado Shooting

Occupy Wall Street

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Colorado Shooting

Occupy Wall Street

CS OWS

Tweets: 122,062 Tweets: 6,077,378

Tags: 192,512 Distinct: 12,350 100% Retrieval: 7,763

Tags: 15,963,209 Distinct: 191,602 100% Retrieval: 21,314

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Colorado Shooting

Occupy Wall Street

CS OWS

Tweets: 122,062 Tweets: 6,077,378

Tags: 192,512 Distinct: 12,350 100% Retrieval: 7,763

Tags: 15,963,209 Distinct: 191,602 100% Retrieval: 21,314

HASHTAG FILTERS 20

Colorado Shooting Occupy Wall Street

Hashtag Filters Co-occurrence Graph

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Colorado Shooting Occupy Wall Street

Event Related Hashtags co-occur

with each other

Hashtag Filters Co-occurrence Graph

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Summarizing Hashtag Analysis

Starting with one of the event relevant hashtags, by co-

occurrence we can reach other relevant hashtags

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Determining Relevancy of Co-occurring Hashtags

#indianelection2015

#modikisarkar

Too many co-occurring hashtags

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Hashtag Filters distributions

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Not surprising It’s a Powerlaw

distribution

Hashtag distributions

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Top 1% retrieves around 85% of the

tweets

Hashtag distributions

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Clustering Co-efficient of Hashtag Co-occurrence network (1%)

Clustering co-efficient

The top ones co-occur with each other the best

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#indianelection2015

#modikisarkar

Co-occurring: Threshold δ

Preferably a prominent hashtag

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Hashtag Co-occurrence works?

o No. Just co-occurrence does not work o Many noisy or unrelated hashtags co-occurs

o Determine the “dynamic” relevance of the top co-occurring hashtag with the dynamic topic

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#indianelection2015

#modikisarkar

Co-occurring: Threshold

Latest K (200,500)

Narendra Modi: 0.9 BJP: 0.7 NDA: 0.6 India: 0.4 Elections: 0.2 Rahul Gandhi: 0.2 Congress: 0.2

Entity Extraction and Scoring

δ

Normalized Frequency Scoring

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(Vector Space Model)

Determining Relevancy of Co-occurring Hashtags (Vector

Space Model) #indianelection2015

#modikisarkar


Latest K (200,500)



Indian General Election,_2014

Dynamically Updated Background Knowledge

δ

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Event Relevant Background Knowledge

o Wikipedia Event Pages

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o Wikipedia Event Pages


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o Entities mentioned on the Event page of Wikipedia are relevant to the Event


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o Wikipedia’s Hyperlink structure is very rich o Page-Page (Wikipedia) links

Indian General Election, 2014

Narendra Modi

Rahul Gandhi

NDA (India) UPA (India)

BJP

Indian National Congress

Event Relevant Background Knowledge – Graph Structure

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#modikisarkar


Latest K (200,500)




Extract, Periodically Update Hyperlink structure

One hop from Event Page

δ

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o Hyperlink structure is dynamically updated


Narendra Modi

Rahul Gandhi


BJP


10 May 2010


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Narendra Modi

Rahul Gandhi


BJP


10 May 2010

29 March 2013

29 March 2013 29 March 2013

29 March 2013


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Narendra Modi

Rahul Gandhi


BJP


10 May 2010

29 March 2013

29 March 2013 29 March 2013

29 March 2013

20 May 2013

20 May 2013


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#modikisarkar


Latest K (200,500)





Entity scoring based on relevance to the Event


δ

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o Edge Based Measure

o Link Overlap Measure: Jaccard similarity

o Out(c) are the links in Wikipedia page “c”

o Final Score: r(c,E) = ed(c,E) + oco(c,E)

Hyperlink Entity Scoring

India General Election, 2014

Narendra Modi



1

Mutually Important

ed (c,E) = 1

ed (c,E) = 2

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#modikisarkar


Latest K (200,500)







Indian General Elec: 1.0 India: 0.9 Elections: 0.7 UPA: 0.6 BJP: 0.3 NDA: 0.3 Narendra Modi: 0.3

δ

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#modikisarkar


Latest K (200,500)








Similarity Check

Relevance Score: 0.6

δ

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o Set Based o Jaccard Similarity

o Considers the entities without the scores

o Vector Based o Symmetric

o Cosine Similarity

o Asymmetric o Subsumption Similarity

Similarity Check

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India General Election 2014

Narendra

Modi

Intuition behind Asymmetric

India General Election 2014

Narendra

Modi

Penalized

Ignored

Similarity

Symmetric

Asymmetric

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#modikisarkar


Latest K (200,500)








Similarity Check

Relevance Score: 0.6

δ

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o 2 events o US Presidential Elections (#election2012)

o Hurricane Sandy (#sandy)

o Top 25 co-occurring hashtags

Evaluation – Dataset

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o Ranking Problem o Rank the Top 25 hashtags based on the relevancy of tweets to the event

o Experiment with all the similarity metrics o Manually annotated the tweets of these hashtags as relevant/irrelevant (Gold Standard)

o Ranking Evaluation Metrics o Mean Average Precision o NDCG

Evaluation – Strategy

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Evaluation

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Evaluation

Evaluated tweets comprising of top-relevant hashtags detected for

dynamic topics • NDCG - 92% at top-5 Mean Average

Precision

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A little pause for Questions?

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Personalized Filtering

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User Interest Identification/User

Modeling

Filtering Module

Twitter Streaming API

Tweets

Network

Filtered Tweets


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Modeling

Filtering Module


Tweets

Network

Filtered Tweets

Dynamic Topics as Interests

Interest: Indian Elections


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Modeling

Filtering Module


Tweets

Network

Filtered Tweets

A Significant Module

o User Interest Identification on Twitter o Content-based (Only Tweets)

o Term-based (semantic, web, #semanticweb)

o Entity-based (sematic web <same as> #semanticweb)

o Interest Graphs derived from knowledge-base (Hierarchical Interest Graphs)

o Collaborative (Users’ Friends)

o Hybrid

User Modeling

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A simple solution to most problems I am trying to solve

Hierarchical Interest Graphs

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What is in your mind? (Next concept/term)

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Fruit

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Fruit

Other Fruit Names

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Cognitive Science

o Human memory has been argued to be structured as a hierarchy of concepts (Semantic Network)

o Spreading activation theory has been

utilized to simulate search on semantic network

o This theory has not been well explored for user interest modeling

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Hierarchical Interest Graphs

o Extending user profiles from Twitter to comprise a hierarchy of concepts

o Hierarchy of concepts are derived from Wikipedia Category Structure

o Each concept in the hierarchy is scored based on the users extent of interest

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Semantic Search

Linked Data Metadata

0.8 0.2 0.6 Scores for Interests

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User Interests

Internet

Semantic Search


Technology

World Wide Web

Semantic Web

Structured Information


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User Interests

Internet

Semantic Search


Technology

World Wide Web

Semantic Web



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User Interests

0.7

0.5

0.4

0.3

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Tweets

Approach

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Tweets

Approach

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Wikipedia Category Graph

Contains Cycles

More abstract: World Wide Web or

Semantic Web?

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Wikipedia Hierarchy

Hierarchical Levels

No Cycles

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2

3

4

5

6

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Tweets

Approach

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http://en.wikipedia.org/wiki/Semantic_search

http://en.wikipedia.org/wiki/Ontology

o Extracting Wikipedia entities

o Interest Scoring o Frequency based

User Profile Generation

Internet

Semantic Search


Technology

World Wide Web

Semantic Web

User Interests



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Tweets

Approach

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Cricket

M S Dhoni Virat Kohli Sachin

Tendulkar

Sports

Indian Cricket

Indian Cricketers

0.8 0.2 0.6

0.5

0.4

0.25

0.1

Activation Function Determines the extent of spreading

Example

o Simple Activation Function

𝐴𝑗 = 𝐴𝑖 ×𝑊𝑖𝑗 × 𝐷𝑛𝑖=0

𝑖 𝑖𝑠 𝑡ℎ𝑒 𝑐ℎ𝑖𝑙𝑑 𝑜𝑟 𝑠𝑢𝑏𝑐𝑎𝑡𝑒𝑔𝑜𝑟𝑦 𝑜𝑓 𝑗 𝐴𝑐𝑡𝑖𝑣𝑎𝑡𝑒𝑑 .

𝑗 𝑖𝑠 𝑡ℎ𝑒 𝑐𝑎𝑡𝑒𝑔𝑜𝑟𝑦 𝑡𝑜 𝑏𝑒 𝑎𝑐𝑡𝑖𝑣𝑎𝑡𝑒𝑑.

𝑊𝑖𝑗 𝑖𝑠 𝑡ℎ𝑒 𝑒𝑑𝑔𝑒 𝑤𝑒𝑖𝑔ℎ𝑡 𝑏𝑒𝑡𝑤𝑒𝑒𝑛 𝑗 𝑎𝑛𝑑 𝑖.

𝐷 𝑖𝑠 𝑡ℎ𝑒 𝑑𝑒𝑐𝑎𝑦 𝑓𝑎𝑐𝑡𝑜𝑟.

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Activation Function

o Uneven distribution of nodes in the hierarchy

oMany-many for category-subcategory relationships

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Challenges – Wikipedia Category Graph



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80 80


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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

0

50000

100000

150000

200000

250000

300000

Nu

mb

er

of N

ode

s

Hierarchical Level

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Addressing Uneven Node Distribution



82 82


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Preferential Path Constraint – Many to Many Links

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1 2 3 4

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Boosting Common Ancestors

o Nodes that intersect domains/subcategories activated by diverse entities

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Cricket

M S Dhoni Virat Kohli Sachin

Tendulkar

Sports

Indian Cricket

Indian Cricketers 3

3

5

5

Michael Clarke

Shane Watson

Australian Cricket

Australian Cricketers

2

2

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88 88


o Bell

𝐴𝑗 = 𝐴𝑖 × 𝐹𝑗

𝑛

𝑖=0

o Bell Log

𝐴𝑗 = 𝐴𝑖 × 𝐹𝐿𝑗

𝑛

𝑖=0

o Priority Intersect

𝐴𝑗 = 𝐴𝑖 × 𝐹𝐿𝑗 × 𝑃𝑗𝑖 × 𝐵𝑗

𝑛

𝑖=0

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Activation Functions

Evaluation

User Study • 37 Users

• 30K Tweets

Evaluated the top-10 categories of interests derived from the hierarchy

• 76% Mean Average Precision • 98% Mean Reciprocal Recall

• 70% are not mentioned in tweets

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oWorking on a Tweet recommendation system that utilizes Hierarchical Interest Graph

o Preliminary results are “interesting”

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Tweet Recommendation using Hierarchical Interest Graph

Conclusion

o Focus on “Information” overload instead of “Data” overload. o Personalized Information Filtering

o Knowledge-base enabled solutions for

challenges in Tweets filtering o Wikipedia hyperlink structure and category

graph leveraged for Twitter data filtering

o More Research on User Specific Attribute Extraction (Personalization) from Twitter Data o Activity Estimation o Location Prediction

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More at Kno.e.sis

kHealth Knowledge-enabled Healthcare

Applied to ADHF, Asthma, GI, and Dementia

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Through physical monitoring and analysis, our cellphones could act as an early warning system to detect serious health conditions, and provide actionable information

canary in a coal mine

Empowering Individuals (who are not Larry Smarr!) for their own health

kHealth: knowledge-enabled healthcare

95

Social Health Signals

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Motivational Scenario

Manually going through news articles, diabetes forums, blogs, etc.

- Time consuming

- Relevant? Interesting? Informative? Useful?

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How about all the relevant and important health

information aggregated at one platform?

A diabetic patient is interested in keeping himself up to date with

new information about diabetes

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Search and Explore

X Controls

Cancer

X = diet, treatment, exercise

(Pattern-based Approach

leveraging domain

semantics)

Top Health News Informative news about selected

disease

Faceted search (by health topics)

Learn about disease

Source: Wikipedia

Search &

Explore Top Health

News

Tweet

Traffic Learn about

Disease Home

Thanks

Contact: [email protected]

Twitter:@pavankaps Webpage:

http://knoesis.org/researchers/pavan

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mailto:[email protected]



knowledge base enabled information filtering on social web -- emc

Social Media

evolving filters

hashtag filters distributions

tweets hashtag distributions

cooccurring hashtags

twitter tweets

event relevant hashtags

prominent hashtag

topic relevant tweets